@@ -39,13 +39,14 @@ kernels support in-place attention during inference and training. They use
implementations, respectively.
-**Efficient alignment scripts** using the original AlphaFold HHblits/JackHMMER pipeline or [ColabFold](https://github.com/sokrypton/ColabFold)'s, which uses the faster MMseqs2 instead. We've used them to generate millions of alignments.
-**FlashAttention** support greatly speeds up MSA attention.
-**DeepSpeed DS4Sci_EvoformerAttention kernel** is a memory-efficient attention kernel developed as part of a new collaboration between OpenFold and DeepSpeed. The kernel provides substantial speedups for training and inference, and significantly reduces the model's peak memory usage. The model is 15% faster during the initial training and finetuning stages, with 40% lower peak memory consumption. To use this feature, set the `use_deepspeed_evo_attention` option in `openfold/config.py`.
## Installation (Linux)
All Python dependencies are specified in `environment.yml`. For producing sequence
alignments, you'll also need `kalign`, the [HH-suite](https://github.com/soedinglab/hh-suite),
and one of {`jackhmmer`, [MMseqs2](https://github.com/soedinglab/mmseqs2)(nightly build)}
installed on on your system. You'll need `git-lfs` to download OpenFold parameters.
installed on your system. You'll need `git-lfs` to download OpenFold parameters.
Finally, some download scripts require `aria2c` and `aws`.
For convenience, we provide a script that installs Miniconda locally, creates a
